Task 1. Flag all the suspicious values. (Outliers, repetitions, etc.)

1. Many months have repetitions (February, march all the districts)
2. Many months have outliers (May, district 4)
3. Some months have very low value (Nov, district 13)
4. Denominator issue (some have more than 100% coverage while some have as low as 58% coverage)

Task 2. Review the national and subnational coverage for MR1. Your data manager produces the following tables. What can you conclude from the administrative data?

There is significant difference between the MR doses administered compared to the surviving infants in that the MR doses are way below the number of surviving infants. This will imply that a good number of the population that should receive the vaccine is not receiving it causing a significant increase in the number of at risk population.

The administrative data indicates that overall coverage in Vacilland is good across all the districts with an exception of Westtan and Nemo that is performing below the administrative target of 80% for subnational across the years apart from 2016 when it managed 82%. Chello and Grandtan also performed below target but picked up in the subsequent years

• Conclusion 1- Since 2011, some of the sub national unit repeatedly reported a coverage > 100%, which looks like an issue with over reporting (especially Grandtown and Remo) and appropriate denominator set up.Grandtown being the Capital of Vacciland is the most affected by fast growing urbanization but if we see the data, target population of surviving infants had been increased at the rate of only 2% annually ignoring the actual population growth due to the given factors.
• Conclusion 2- Units like Nemo, Westtan always reporting a low coverage which indicates a potential denominator issue as well as access/utilization issue of vaccination.
• Conclusion 3- Nationally MR1 coverage remains low except for 2016. This is an indication of potential immunity gap over the years

Task 3. Review coverage evaluation survey data. You remember that in 2013, there was a coverage evaluation survey. You pull up the data for that. Does this change your view about coverage at national level? For any of the regions?

• If we compare the national coverage data from the survey in 2013 with the administrative data from the same year, there is no significant difference; the coverage is quite similar. However data at the region level, almost all regions have different coverage data compared to administrative data. Regions of Alu, Grandtown and Remo have significant lower coverage data in survey data. While, regions of Nemo, Chello, Grandtan and Westtan have higher coverage data in survey data. None of region has coverage >95%. Having national coverage <95% implicates that there immunity gap in the population. and this implies the same with Grandtown region as its coverage at 89%, makes it still prone/vulnerable to VPDs.
• Estimated coverage of Nemo & Westtan is confirming that targets are not accurately estimated.
• There some issues in the administrative coverage of province Remo as there is a big difference between admin & estimated coverage.
• There are some population shifts within the country so overall both the reported & estimated coverages are matching.

Task 4. Review the chart with the age distribution of measles cases. Does that tell you anything additional about coverage?

The age distribution chart depicts that 11% of the measles cases are in the age group of less than 1 year (refer table below). This means there is a lag in MR1 vaccine administration. This could be due to vaccine hesitancy.

The chart reveals that, children 1-4 years were the most affected age group which requires further investigation considering the MR immunization coverage for the year. It also shows the most affected different age groups which could be considered for immunization against the disease.

Overall 37% confirmed measles cases are <5 years of age which were born after 2011 Measles SIA. More than 80% of the cases are either zero dose or having un-known vaccination status and paper based coverage data collection and reporting system having issues of outdated registers, weak capacities of lower EPI staff indicate that coverage data does not reflect the actual picture.

It is obvious to see that children under 5 years are more vulnerable to be contacted by measles and this indicates that the vaccination coverage is lower than the reported coverage which resulted in accumulation of susceptible children/pockets resulting in outbreaks. So this puts question marks over reported data.

Part 2. Brief the Minister

Task 5. Brief the Minister (spend max 1/2 hour on this section). Summarize the situation in three bullet points.

• A massive measles outbreak in Vacciland is reported having highest number of measles cases in past 5 years in the country. So far, 625 cases have been confirmed, and a number of child deaths have also reported. Highest numbers of cases are confirmed from the Capital Grandtown. Whereas highest number of cases are under five years of age.
• Although the reported coverage is showing higher coverage rates at National and Sub-national levels; but the number of cases is indicating serious coverage gaps at district level as well as significant difference of coverage at sub-national levels. So we are facing serious data quality issues as well.
• Rapidly increasing urbanization and underserved population in pockets of slums across the cities and outskirts with poor communities living are having serious immunity gaps and are affected by outbreak the most, which needs quick response.
• Vacciland has potential denominator issue where some unit has low denominator resulting in high coverage and missing children. Also there is some unit has low coverage though survey data shows a high coverage. Denominator needs to set up correctly

Task 6. Brief the Minister. Propose three actions to respond to the outbreak

• To do MR Campaign and ensure 100% coverage
• To do epidemiologic investigation and strengthen the surveillance system
• To strengthen the routine immunization
• To strengthen the quality of data
• There should be a review of data management tools and an urgent Data Management re-training for relevant officers at the district & regional levels
• There is need for enhanced monitoring activities and data validation between Administrative & Monitoring data submissions
• Vigorous social mobilization activities should be conducted to enhance community participation with involvement of all line departments.
• Vaccine accountability/ supply: The stock status has to be maintained and put in place an accountability frame work that would monitored cold chain management has to be stable

Task 7. Formulate recommendations. List your top 3-5 recommendations specific to data strengthening you would prioritize as the EPI and surveillance teams in Vacciland

1. To fix the denominator
2. Building the capacity of health workers to reduce data duplication, fix the recording format and the reporting line
3. Monthly data verification meeting with all EPI and surveillance team members at district, county and national level
4. Involvement of local stakeholders in microplanning activities on immunization
5. Monthly supportive supervision at facility, district and county level on immunization data